Impact and Detection of Facial Beautification in Face Recognition: An Overview

Rathgeb, Christian; Dantcheva, Antitza; Busch, Christoph

doi:10.1109/access.2019.2948526

Cited by 63 publications

(55 citation statements)

References 80 publications

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“…eyes, lips, and skin. Prominent examples of makeup alterations include changing of the perceived contrast of the eyes, size of the mouth, as well as skin colour [15], [16]. Further, the application of makeup can be categorised w.r.t.…”

Section: A Makeup Presentation Attacksmentioning

confidence: 99%

“…In case the use of facial makeup has been detected in a captured facial image, the face recognition system can react accordingly, e.g. by applying the feature extraction with different parameters [16].…”

Section: A Makeup Presentation Attacksmentioning

confidence: 99%

“…Makeup may substantially alter the perceived facial texture and shape which can pose a challenge to automated face recognition [15], [16]. Most prominently covered in the media, the Computer Vision Dazzle Camouflage campaign [17] showed how hairstyling and makeup designs can be used for identity concealment, i.e.…”

Section: Introductionmentioning

confidence: 99%

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“…fluctuations in pose, facial expression, or image quality [3], [10]. Additionally, facial beautification [11] induced by plastic surgery, cosmetics, or beautification in the digital domain, i.e. retouching, was determined to be able to significantly alter the perceived shape and texture of a human face and therefore to negatively affect the accuracy of face recognition systems.…”

Section: Introductionmentioning

confidence: 99%

“…Most relevant works on the impact and detection of facial retouching in face recognition (adapted from[11]). …”

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Differential Detection of Facial Retouching: A Multi-Biometric Approach

et al. 2020

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Facial retouching apps have become common tools which are frequently applied to improve one's facial appearance, e.g. before sharing face images via social media. Beautification induced by retouching has the ability to substantially alter the appearance of face images and hence might represent a challenge for face recognition. Towards deploying secure face recognition as well as enforcing antiphotoshop legislations, a robust and reliable detection of retouched face image is needed. Published approaches consider a single image-based (no-reference) scenario where a potentially retouched face image serves as sole input to the retouching detector. However, in many cases a trusted unaltered face image of a subject examined is available which enables an image pair-based (differential) detection scheme. In this work, ICAO-compliant subsets of the FERET and FRGCv2 face databases are used to automatically create a database containing 9,078 retouched face images together with unconstrained probe images. In evaluations employing the commercial Cognitec FaceVACS and the open-source ArcFace face recognition system, it is shown that facial retouching can negatively impact face recognition performance. Further, a differential facial retouching detection system is proposed which processes pairs of a potentially retouched reference image and corresponding unaltered probe image of single subjects. Estimated differences in feature vectors obtained from texture descriptors, facial landmarks, and deep face representations are leveraged by machine learning-based classifiers of which the detection scores are fused to distinguish between retouched and unaltered face images. The proposed scheme is evaluated in a cross-database scenario where training and testing are performed on the FERET and FRGCv2 databases and vice versa. In the scenario where the used retouching algorithm is known by the detection algorithm, a competitive average D-EER of approximately 2% is achieved. Further, the scenario in which the employed retouching algorithm is not known by the detection algorithm is evaluated. In the latter scenario, the proposed approach obtains an average D-EER below 10% and is shown to outperform several state-of-the-art single image-based detection schemes.

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Facial Retouching and Alteration Detection

Majumdar

Vatsa

Singh

2022

Handbook of Digital Face Manipulation and Detection

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On the social media platforms, the filters for digital retouching and face beautification have become a common trend. With the availability of easy-to-use image editing tools, the generation of altered images has become an effortless task. Apart from this, advancements in the Generative Adversarial Network (GAN) leads to creation of realistic facial images and alteration of facial images based on the attributes. While the majority of these images are created for fun and beautification purposes, they may be used with malicious intent for negative applications such as deepnude or spreading visual fake news. Therefore, it is important to detect digital alterations in images and videos. This chapter presents a comprehensive survey of existing algorithms for retouched and altered image detection. Further, multiple experiments are performed to highlight the open challenges of alteration detection.

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Impact and Detection of Facial Beautification in Face Recognition: An Overview

Cited by 63 publications

References 80 publications

Makeup Presentation Attacks: Review and Detection Performance Benchmark

Makeup Presentation Attacks: Review and Detection Performance Benchmark

Differential Detection of Facial Retouching: A Multi-Biometric Approach

Facial Retouching and Alteration Detection

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